In this paper, we focus on exploring the robustness of the 3D object
detection in point clouds, which has been rarely discussed in existing
approaches. We observe two crucial phenomena: 1) the detection accuracy of the
hard objects, e.g., Pedestrians, is unsatisfactory, 2) when adding additional
noise points, the performance of existing approaches decreases rapidly. To
alleviate these problems, a novel TANet is introduced in this paper, which
mainly contains a Triple Attention (TA) module, and a Coarse-to-Fine Regression
(CFR) module. By considering the channel-wise, point-wise and voxel-wise
attention jointly, the TA module enhances the crucial information of the target
while suppresses the unstable cloud points. Besides, the novel stacked TA
further exploits the multi-level feature attention. In addition, the CFR module
boosts the accuracy of localization without excessive computation cost.
Experimental results on the validation set of KITTI dataset demonstrate that,
in the challenging noisy cases, i.e., adding additional random noisy points
around each object,the presented approach goes far beyond state-of-the-art
approaches. Furthermore, for the 3D object detection task of the KITTI
benchmark, our approach ranks the first place on Pedestrian class, by using the
point clouds as the only input. The running speed is around 29 frames per
second.

本文探究了点云中三维物体检测的鲁棒性问题，引入了一种新的 TANet 模型，其中 Triple Attention 模块和 Coarse-to-Fine Regression 模块的结合提高了模型性能，实验结果在 KITTI 数据集上排名第一，较之现有最先进模型运行速度更快，且准确性更高。